Fluid Machinery By Jose Francisco Pdf < 2025-2026 >

app = FastAPI() cache = redis.from_url(os.getenv("REDIS_URL"))

"mappings": "properties": "content": "type": "text", "analyzer": "standard" , "equation_latex": "type": "text", "analyzer": "latex_analyzer" , "page_number": "type": "integer" , "settings": "analysis": "analyzer": "latex_analyzer": "tokenizer": "standard", "filter": ["lowercase", "latex_symbols"] , "filter": "latex_symbols": "type": "pattern_replace", "pattern": "[^\\\\a-zA-Z0-9]", "replacement": " " Fluid Machinery By Jose Francisco Pdf

// src/api.ts export const search = (query: string) => axios.get('/api/search', params: q: query ); export const getEquation = (eqId: string) => axios.get(`/api/equation/$eqId`); export const summarize = (pageRange: string) => axios.post('/api/ai/summary', pages: pageRange ); export const generateQuiz = (chapter: number) => axios.post('/api/ai/quiz', chapter ); export const exportPack = (payload) => axios.post('/api/export', payload, responseType: 'blob' ); Custom Analyzer – tokenizes on whitespace and on LaTeX delimiters ( $ , \ , , ). Fields – content , equation_latex , page_number . app = FastAPI() cache = redis

@app.post("/summary") def summary(pages: dict = Body(...)): text = pages["text"] prompt = f"Summarize the following text from *Fluid Machinery* in ≤ 5 bullet points.\n\nText:\ntext" return "summary": call_llm(prompt) "equation_latex": "type": "text"

def call_llm(prompt: str, temperature=0.2): cache_key = f"llm:hash(prompt)" if cached := cache.get(cache_key): return cached.decode() response = openai.ChatCompletion.create( model="gpt-4o", messages=["role": "user", "content": prompt], temperature=temperature, ) result = response.choices[0].message.content cache.setex(cache_key, 86400, result) # 24‑h cache return result

# ai_gateway/main.py from fastapi import FastAPI, Body import openai, os, redis